Correction of crop water deficit indicators based on time-lag effects for improved farmland water status assessment

Crop water deficit indicators such as crop water stress index (CWSI), actual crop evapotranspiration (ET), and stomatal conductance (gs) are widely utilized for soil water content (SWC) monitoring. However, time-lag effects between canopy temperature (Tc) and environmental factors can influence their correlation with SWC, thereby complicating the identification of the most reliable diagnostic indicator. This study conducted a two-year field experiment on winter wheat under four irrigation levels (80–95 %, 65–80 %, 50–65 %, and 40–50 % field capacity). Time-lag cross-correlation, time-lag mutual information, grey time-lag correlation analysis, time-lag Almon, and time-lag partial least squares (PLS) were applied to calculate the time-lag parameters. These time-lag parameters were subsequently used to correct the correlations between CWSI, ET, gs, and SWC. The indicator with the strongest correlation to SWC was selected and then predicted using four machine learning models. Results demonstrated that time-lag correction significantly enhanced the correlation between SWC and theoretical CWSI, empirical CWSI, gs, and ET, with increases of 0.15, 0.33, 0.11, and 0.21, respectively; Time-lag mutual information exhibited the highest effectiveness in correcting time-lag effects; The sudden decline in gs and the peak advancement in severe water stress treatments led to abrupt changes in time-lag parameters; The Convolutional Neural Network-Bidirectional Long Short-Term Memory-Adaptive Boosting model achieved the highest accuracy in predicting gs corrected by time-lag mutual information from 8:00–15:00 (R 2 =0.96). These results provided a theoretical foundation for accurately assessing soil moisture conditions in agricultural fields and contributed to advancing water conservation techniques in arid farmland. • Five methods were employed to quantify the time-lag effects. • Time-lag mutual information most effectively corrected the time-lag effects. • The stomatal conductance (gs) after time-lag correction was the optimal indicator. • The abrupt change and early peak of gs induced a sudden shift in time-lag parameters. • Four machine learning models were utilized to predict gs after time-lag correction.